DNA segmentation as a model selection process
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Fuzzy Systems Handkbook with Cdrom
The Fuzzy Systems Handkbook with Cdrom
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Regulatory Element Detection Using a Probabilistic Segmentation Model
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A fuzzy guided genetic algorithm for operon prediction
Bioinformatics
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
An evolutionary-based approach for solving a capacitated hub location problem
Applied Soft Computing
Hi-index | 0.00 |
The fuzzy-driven genetic algorithm for sequence segmentation consists of a genetic algorithm whose objective function is driven by a fuzzy fitness finder. The genetic algorithm starts with an initial population of alternate solutions where each solution is a different partitioning of the sequence into segments. The algorithm uses adaptations of the standard genetic operators to reallocate the partitions so as to achieve optimal segmentations. A fuzzy fitness finding mechanism evaluates the fitness values of the evolving segmentations, taking into consideration the combined effect of multiple heterogeneous features that have been identified as governing factors for the formation of the segments. The relationships between segment elements can also be modeled by this novel approach of applying soft computing paradigms to the segmentation of multi-dimensional sequences. The algorithm developed in this work has been successfully implemented for gene sequence segmentation to predict groups of functionally related genes that lie adjacent on the genome sequences of bacterial genomes.